Electric automation equipment fault detection system

Abstract

The invention, which relates to the fault detection field, discloses an electrical automation equipment fault detection system comprising a fault detection management center comprising an equipment management module, a data acquisition module, a data preprocessing module, a fault analysis module and a fault early warning module. The equipment management module is used for acquiring electrical automation equipment information; the data acquisition module is used for acquiring equipment operation information and setting a fault diagnosis link; the data preprocessing module is used for constructing a comprehensive diagnosis knowledge link of a fault type corresponding to the information of each piece of electrical automation equipment, and generating a self-adaptive diagnosis knowledge link according to the fault diagnosis link and the comprehensive diagnosis knowledge link; the fault analysis module is used for performing fuzzy processing on corresponding fault diagnosis feature data in the obtained adaptive diagnosis knowledge link to generate an adaptive fault analysis link; and the fault early warning module is used for performing fault detection processing on the fault diagnosis link according to the adaptive fault analysis link to generate fault detection information.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and particularly to a fault detection system for electrical automation equipment. Background Art

[0002] In today's highly industrialized era, electrical automation equipment is widely used in various fields, such as manufacturing, energy industry, transportation industry, etc. The stable operation of these equipment plays a crucial role in ensuring production efficiency, improving product quality, reducing production costs, and ensuring production safety. Once a fault occurs in the electrical automation equipment, it may lead to production interruption, product quality decline, equipment damage, and even safety accidents, bringing huge economic losses and adverse effects to enterprises; this fault detection system aims to monitor the operating state of electrical automation equipment in real time, quickly and accurately detect faults, so as to ensure the continuity and stability of production, and reduce equipment maintenance costs and production losses.

[0003] After retrieval, the invention patent with the Chinese patent number CN111693822B discloses a fault detection system for electrical equipment lines based on a cloud platform, which uses a signal acquisition module to collect the detection signals, time points, and line position data transported by the line body; uses a signal processing module to preprocess the collected detection signals, time points, and line position data, which can effectively improve the efficiency of processing fault signal data and facilitate timely searching for fault information data; uses a signal analysis module to analyze the obtained combined detection set, uses a preset discontinuous condition to screen and judge the discontinuous values, and saves the discontinuous values that do not meet the discontinuous condition to obtain a fault data set; uses a display module to display the line position data of the fault data set, and uses the line position data to perform fault detection and troubleshooting on the fault data set, overcoming the problems of complex detection steps and inability to accurately display the position of the fault line in the existing solutions.

[0004] Compared with the prior art, the invention patent with the Chinese patent number CN111693822B analyzes and processes the detection signals, time points, and line position information corresponding to the electrical equipment lines, sets corresponding data sets, and performs fault detection and troubleshooting through the corresponding data sets, thereby improving the accuracy in the process of excluding the positions of the corresponding fault lines.

[0005] However, in the actual use process of the above system, it can only compare and analyze the obtained detection signals in sequence by setting data sets, so as to exclude the faults of the corresponding electrical equipment, which causes a certain degree of resource waste to a certain extent, and has the disadvantages of low efficiency and insufficient coordination in the process of detecting electrical equipment faults. Summary of the Invention

[0006] The object of the present invention is to solve the disadvantages of low efficiency and resource waste in the prior art, and to propose a fault detection system for electrical automation equipment.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A fault detection system for electrical automation equipment, including a fault detection management center, which includes an equipment management module, a data acquisition module, a data preprocessing module, a fault analysis module, and a fault warning module;

[0009] The equipment management module is used to obtain the production process information corresponding to the corresponding enterprise, and obtain the corresponding electrical automation equipment information according to the production process information;

[0010] The data acquisition module is used to collect the equipment operation information during the operation of the electrical automation equipment information corresponding to the production process information, and set the collected equipment operation information as a fault diagnosis link;

[0011] The data preprocessing module is used to comprehensively process the historical equipment operation information corresponding to the corresponding electrical automation equipment information, construct a comprehensive diagnosis knowledge link of the fault type corresponding to the corresponding electrical automation equipment information, match the fault diagnosis link with the corresponding comprehensive diagnosis knowledge link, and generate an adaptive diagnosis knowledge link according to the matching result;

[0012] The fault analysis module is used to perform fuzzy processing on the corresponding fault diagnosis feature data in the obtained adaptive diagnosis knowledge link, and generate an adaptive fault analysis link according to the fuzzy processing result;

[0013] The fault warning module is used to compare and analyze the obtained fault diagnosis link with the corresponding adaptive fault analysis link, and generate fault detection information according to the comparison and analysis result.

[0014] The above technical solution further includes: The process of obtaining electrical automation equipment information includes:

[0015] The corresponding enterprise production information is input by the staff in the enterprise. The enterprise production information includes enterprise qualification information, enterprise registration information, production basic information, and production process information, and the obtained enterprise production information is verified;

[0016] Obtain the enterprise production information that has completed the verification process, analyze and process the corresponding production basic information and production process information, obtain the production process information corresponding to the corresponding production product information. The production process information includes the production process information corresponding to the corresponding product, the production equipment information corresponding to the production process information, and the production equipment utilization information corresponding to the production process information. According to the production process information, obtain the electrical automation equipment information corresponding to the corresponding production process information, and generate corresponding process monitoring nodes.

[0017] Furthermore, the process of obtaining the equipment operation information of the corresponding electrical automation equipment during operation includes:

[0018] An operation monitoring terminal is set in each electrical automation equipment. The operation monitoring terminal is used to collect the equipment operation information of the electrical automation equipment and is interconnected with the corresponding process monitoring nodes.

[0019] Set a passive acquisition unit and an active acquisition unit;

[0020] The passive acquisition unit is used for staff to input acquisition requirement information, and obtain the equipment operation information corresponding to the corresponding electrical automation equipment according to the acquisition requirement information;

[0021] The active acquisition unit is used to collect in real time the equipment operation information during the operation of the corresponding electrical automation equipment in the production process information;

[0022] The equipment operation information includes electrical parameter data, equipment status data, and historical fault data, and the obtained equipment operation information is marked according to the corresponding acquisition time and electrical automation equipment information.

[0023] Furthermore, the process of setting a fault diagnosis link according to the equipment operation information includes:

[0024] Set a fault diagnosis cycle, obtain the marking result of the equipment operation information, obtain the equipment operation information whose corresponding acquisition time belongs to the fault diagnosis cycle, sort the equipment operation information in sequence according to the process monitoring nodes to which the equipment operation information belongs, integrate the obtained equipment operation information according to the sorting result, generate a corresponding fault diagnosis link, and temporarily store the obtained equipment operation information through the fault diagnosis link.

[0025] Furthermore, the process of constructing a comprehensive diagnosis knowledge link for the fault type corresponding to the corresponding electrical automation equipment information includes:

[0026] Set up a production process link according to the obtained production process information based on the corresponding process monitoring nodes, mark the overlapping situations of the electrical automation devices belonging to the corresponding process monitoring nodes within each production process link, and set up comprehensive diagnosis nodes according to the corresponding electrical automation devices;

[0027] Horizontally connect according to the comprehensive diagnosis nodes corresponding to the corresponding production process links, and vertically connect the corresponding process monitoring nodes within the corresponding comprehensive diagnosis nodes to generate an initial connection map;

[0028] Perform correlation processing according to the connection situations of the corresponding process monitoring nodes within the initial connection map, and obtain the historical state data sets corresponding to the corresponding fault types of the corresponding process monitoring nodes. The historical state data sets include historical state data and corresponding analysis results;

[0029] Set up corresponding horizontal comparison subsets and vertical comparison subsets respectively according to the horizontal connection situations and vertical connection situations of the corresponding process monitoring nodes;

[0030] Obtain the comparison correlation data corresponding to the corresponding horizontal comparison subsets and vertical comparison subsets, and set up corresponding comparison correlation thresholds respectively according to the horizontal connection situations and vertical connection situations within the corresponding initial connection map;

[0031] Compare and analyze the obtained comparison correlation data with the corresponding comparison correlation thresholds, and perform correlation marking on the corresponding process monitoring nodes within the corresponding comparison subsets according to the comparison and analysis results;

[0032] Mark within the initial connection map according to the correlation marking results to generate a comprehensive diagnosis knowledge link.

[0033] Furthermore, the process of generating an adaptive diagnosis knowledge link includes:

[0034] Obtain a fault diagnosis link, perform matching analysis on the device operation information stored within the fault diagnosis link and the comprehensive diagnosis knowledge link, and extract the process monitoring nodes with correlation markings within the corresponding comprehensive diagnosis nodes in the comprehensive diagnosis knowledge link according to the matching analysis results;

[0035] Extract the connection relationships and position information within the comprehensive diagnosis knowledge link to which the obtained process monitoring nodes belong, and obtain the corresponding adaptive diagnosis knowledge link according to the extraction results.

[0036] Furthermore, the process of generating an adaptive fault analysis link includes:

[0037] Obtain the adaptive diagnosis knowledge link corresponding to the corresponding fault diagnosis link. The adaptive diagnosis knowledge link includes the fault diagnosis feature data corresponding to the corresponding fault types of the corresponding electrical automation devices;

[0038] Summarize and integrate the corresponding fault diagnosis feature data according to the connection of the process monitoring nodes corresponding to the fault types in the adaptive diagnosis knowledge link, and set up a fault handling data set;

[0039] Compare and analyze the corresponding fault diagnosis feature data in the fault handling data set pairwise according to the corresponding data types, obtain the corresponding relative membership matrix, and obtain the membership data corresponding to the corresponding data types in the fault handling data set according to the obtained relative membership matrix;

[0040] Preset a fuzzy rule base, input the obtained membership data into the fuzzy rule base for matching analysis, and calculate the fuzzy sets corresponding to the corresponding fuzzy logic operators;

[0041] Set the fuzzy processing results of each fault diagnosis feature data in the corresponding adaptive diagnosis knowledge link according to the corresponding fuzzy sets, and associate and store the corresponding fuzzy processing results with the corresponding adaptive diagnosis knowledge link to generate an adaptive fault analysis link.

[0042] Furthermore, the process of generating fault detection information includes:

[0043] Obtain the device operation information corresponding to the corresponding electrical automation device information in the fault diagnosis link, compare and analyze the obtained device operation information with the fuzzy processing results of the corresponding fault diagnosis feature data in the adaptive diagnosis knowledge link, obtain the matching results of the corresponding fault types, and perform data marking on the matching mark results;

[0044] Obtain the data marking results corresponding to the corresponding fault types in the adaptive fault analysis link, and preset the corresponding fault evaluation coefficients according to the control association data corresponding to the associated marks between the corresponding process monitoring nodes in the adaptive fault analysis link;

[0045] Judge whether the corresponding electrical automation device conforms to the corresponding fault type according to the evaluation results of the corresponding process monitoring nodes and the fault evaluation coefficients;

[0046] Traverse and analyze the fault diagnosis feature data corresponding to each fault type in turn, and obtain the fault types of the corresponding electrical automation devices according to the traversal analysis results;

[0047] Output the fault detection information of the corresponding electrical automation device according to the obtained fault types.

[0048] The present invention has the following beneficial effects:

[0049] 1. In the present invention, by setting up a passive acquisition unit and an active acquisition unit to acquire the equipment operation information of corresponding electrical automation equipment during operation, the initiative of obtaining equipment operation information can be improved to a certain extent, avoiding the process of solely monitoring the operation of electrical automation equipment to obtain equipment operation information, thereby avoiding the limitations in the process of collecting equipment operation information. By setting up a fault diagnosis link to uniformly store and process the equipment operation information obtained within the same period, the integrity of reference data in the fault detection process is improved to a certain extent.

[0050] 2. In the present invention, by performing correlation analysis on the equipment operation information corresponding to each electrical automation equipment when a fault occurs during operation in the corresponding production process information and the equipment operation information corresponding to the same electrical automation equipment for different production products, a corresponding comprehensive diagnosis knowledge link is constructed. Through the comprehensive diagnosis knowledge link, correlation analysis is performed on the fault data corresponding to each electrical automation equipment in the corresponding enterprise. The obtained equipment operation information is adaptively analyzed through the fault diagnosis link, and the fault diagnosis feature data associated in the comprehensive diagnosis knowledge link is extracted according to the adaptive analysis result, thereby improving the analysis efficiency in the fault detection process to a certain extent. By using other associated electrical automation equipment to assist in analyzing the equipment operation information corresponding to the corresponding electrical automation equipment, the accuracy in the fault detection process is also improved to a certain extent.

[0051] 3. In the present invention, the obtained adaptive diagnosis knowledge link is analyzed and processed to obtain the fuzzy processing results of each fault diagnosis feature data in the corresponding adaptive diagnosis knowledge link. The obtained equipment operation information is analyzed and processed with the fuzzy processing results of the corresponding fault diagnosis feature data, avoiding setting precise thresholds to judge whether the corresponding electrical automation equipment has the corresponding fault type, and performing multi-party assisted detection through the analysis results corresponding to each association relationship, thereby reducing the analysis error of electrical automation equipment in the fault detection process to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a block diagram of a fault detection system for an electrical automation equipment proposed by the present invention;

[0053] Figure 2 is a flowchart of the implementation steps of a fault detection system for an electrical automation equipment proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] Embodiment 1

[0056] As Figures 1 to 2 shown, a fault detection system for an electrical automation device proposed by the present invention includes a fault detection management center, and the fault detection management center includes a device management module, a data acquisition module, a data preprocessing module, a fault analysis module, and a fault warning module.

[0057] In this embodiment, the fault detection management center is used to perform fault detection management on the corresponding electrical automation device. By real-time monitoring the corresponding parameters in the operation process of the corresponding electrical automation device, analyzing and processing the real-time monitoring results, judging whether there are fault behaviors in the operation process of the corresponding electrical automation device, and analyzing and processing the corresponding fault behaviors, so as to reduce the impact of the corresponding electrical automation device fault on the enterprise production process. Its specific implementation process includes:

[0058] The device management module is used to obtain the production process information corresponding to the corresponding enterprise, and obtain the corresponding electrical automation device information according to the production process information;

[0059] The data acquisition module is used to collect the device operation information of the electrical automation device corresponding to the production process information during operation, and set the collected device operation information as a fault diagnosis link;

[0060] The data preprocessing module is used to comprehensively process the historical device operation information corresponding to the corresponding electrical automation device information, construct a comprehensive diagnosis knowledge link of the fault type corresponding to the corresponding electrical automation device information, match the fault diagnosis link with the corresponding comprehensive diagnosis knowledge link, and generate an adaptive diagnosis knowledge link according to the matching result;

[0061] The fault analysis module is used to perform fuzzy processing on the corresponding fault diagnosis feature data in the obtained adaptive diagnosis knowledge link, and generate an adaptive fault analysis link according to the fuzzy processing result;

[0062] The fault warning module is used to compare and analyze the obtained fault diagnosis link with the corresponding adaptive fault analysis link, and generate fault detection information according to the comparison and analysis result.

[0063] The process of obtaining production process information, obtaining corresponding electrical automation equipment information based on the production process information, and collecting equipment operation information of corresponding electrical automation equipment according to the obtained electrical automation equipment information includes:

[0064] An enterprise management unit and a production management unit are set in the equipment management module;

[0065] The enterprise management unit is used for staff in the corresponding enterprise to input corresponding enterprise production information, obtain the enterprise production information corresponding to the corresponding enterprise according to the input situation of the staff. The enterprise production information includes enterprise qualification information, enterprise registration information, production basic information, and production process information, where:

[0066] Enterprise qualification information includes various qualification information such as business qualification information, industry access qualification information, and quality certification qualification information corresponding to the corresponding enterprise;

[0067] Enterprise registration information includes various qualification information such as basic enterprise information, registered address and contact information, enterprise type, business scope, shareholder and capital contribution information corresponding to the corresponding enterprise;

[0068] Production basic information includes corresponding production product information and product equipment information, etc.;

[0069] Production process information includes production process information, process quality management information, and production equipment utilization information corresponding to the corresponding product production process;

[0070] The enterprise management unit conducts verification and analysis on the corresponding enterprise production information, and sends the enterprise production information that has completed verification processing to the production management unit according to the verification and analysis results;

[0071] The production management unit obtains the enterprise production information that has passed the verification process, analyzes and processes the corresponding production basic information and production process information in the enterprise production information, analyzes and processes the product equipment information, production process information, and production equipment utilization information corresponding to the corresponding production product information in the production basic information and production process information, and obtains the production process information corresponding to the corresponding production product information. The production process information includes production process information corresponding to the corresponding product, production equipment information corresponding to the production process information, and production equipment utilization information corresponding to the production process information;

[0072] Set corresponding process monitoring nodes in sequence according to the production process information corresponding to the production process information, obtain the electrical automation equipment information corresponding to the corresponding production process information, associate the corresponding process monitoring nodes with the corresponding electrical automation equipment information, and perform marking processing on the obtained electrical automation equipment information according to the association result.

[0073] The data acquisition module is used to collect data information during the operation of corresponding electrical automation equipment information according to the production process information corresponding to the corresponding production product information. The specific implementation process includes:

[0074] A passive acquisition unit and an active acquisition unit are arranged in the data acquisition module;

[0075] The passive acquisition unit is used for corresponding staff in the enterprise to input acquisition requirement information, and the acquisition requirement information includes various types of requirement information such as equipment maintenance requirement types, quality control requirement types, and enterprise decision-making requirement types;

[0076] According to the requirement type corresponding to the acquisition requirement information, corresponding staff input requirement detail information, and the requirement detail information includes corresponding electrical automation equipment model information, operation detail information, acquisition requirement information, etc.;

[0077] Match the requirement detail information input in the acquisition requirement information with the process monitoring nodes corresponding in the production process information, and send the corresponding acquisition requirement information to the corresponding process monitoring nodes according to the matching result. The process monitoring nodes are interconnected with the operation monitoring terminals corresponding to the corresponding electrical automation equipment, and the equipment operation information of the electrical automation equipment is obtained through the operation monitoring terminals;

[0078] The operation monitoring terminal includes a data acquisition card and corresponding sensor devices, and the equipment operation information includes electrical parameter data, equipment status data, and historical fault data;

[0079] The active acquisition unit is used to collect the equipment operation information corresponding to the electrical automation equipment operating during the production of corresponding enterprise products. When the corresponding electrical automation equipment is operating, connect to the corresponding process monitoring node of the corresponding electrical automation equipment through the active acquisition unit, and obtain the equipment operation information corresponding to the corresponding electrical automation equipment through the corresponding process monitoring node;

[0080] Mark the obtained equipment operation information, and send it to other modules for analysis and processing according to the marking result.

[0081] The data preprocessing module is used to comprehensively process the equipment operation information corresponding to the corresponding electrical automation equipment information, set an adaptive diagnosis knowledge link, and send the adaptive diagnosis knowledge link to the fault analysis module. The specific implementation process includes:

[0082] An equipment preprocessing unit and a data preprocessing unit are arranged in the data preprocessing module;

[0083] The device preprocessing unit is used to obtain all the device operation information collected by the data acquisition module, comprehensively process the collected device operation information, and generate a comprehensive diagnosis knowledge link according to the comprehensive processing result. The specific implementation process includes:

[0084] Obtain the types of production product information corresponding to the corresponding enterprise and the production process information corresponding to each type of production product information, and according to the process monitoring nodes corresponding to the electrical automation equipment in the corresponding production process information;

[0085] Set corresponding production process links according to the process monitoring nodes corresponding to the electrical automation equipment for the obtained production process information, and perform marking processing on each production process link;

[0086] Perform marking processing on the production process links that have completed the marking processing according to the overlapping situation of the process monitoring nodes corresponding to the corresponding electrical automation equipment, and set comprehensive diagnosis nodes for the corresponding electrical automation equipment according to the marking processing results of the corresponding overlapping situation. The comprehensive diagnosis nodes are used to manage the process monitoring nodes corresponding to each production process information in the corresponding electrical automation equipment respectively;

[0087] Horizontally connect each production process link according to the connection situation of the corresponding process monitoring nodes in the comprehensive diagnosis nodes corresponding to the production process links corresponding to the marking results;

[0088] Vertically connect the corresponding process monitoring nodes in the corresponding comprehensive diagnosis nodes;

[0089] The corresponding enterprise integrates according to the horizontal connection situation between the comprehensive diagnosis nodes corresponding to the production process information and the vertical connection situation within the comprehensive diagnosis nodes to generate an initial connection map;

[0090] Analyze and process the initial connection map, integrate the data information corresponding to the horizontal connection situation and the vertical connection situation, and generate a comprehensive diagnosis knowledge link according to the integration result. The specific implementation process includes:

[0091] Obtain the process monitoring nodes corresponding to the horizontal connection situation and the vertical connection situation in the corresponding initial connection map, obtain the corresponding historical operation status data and the analysis results of the historical operation status data in the process monitoring nodes, and set corresponding historical status data sets according to the obtained historical operation status data and the analysis results corresponding to the historical operation status data;

[0092] Associate the historical status data sets set in each process monitoring node respectively according to the corresponding horizontal connection situation and vertical connection situation;

[0093] Obtain the corresponding fault types in the historical status data set set for the monitoring nodes of the corresponding processes. According to the historical status data corresponding to the corresponding fault types and their corresponding analysis results, set the horizontal comparison subset and the vertical comparison subset respectively according to the horizontal connection situation and the vertical connection situation;

[0094] The horizontal comparison subset includes the data analysis process corresponding to the process monitoring nodes corresponding to the corresponding horizontal connection situation. The vertical comparison subset includes the data analysis process corresponding to the process monitoring nodes corresponding to the corresponding vertical connection situation. Mark the corresponding data types in the historical operation status data respectively as A1, A2, …, A m and B1, B2, …, B m , where m is the number of types of the corresponding operation status data. Among them, A and B are the marks of the two process monitoring nodes corresponding to the horizontal comparison subset respectively;

[0095] Set the corresponding variable sets A 1n and B 1n for the operation status data of the type corresponding to the corresponding operation status data. Among them, A 1n = a 11 , a 12 , a 1i , a 1n ; B 1n = b 11 , b 12 , b 1i , b 1n ;

[0096] Mark the type reference association data of the corresponding data type in the corresponding horizontal comparison subset as CG m , and mark the comparison association data as DG, where:

[0097] γ m is the weight reference coefficient of the corresponding operation status data type;

[0098] Analyze and process the comparison association data obtained from each comparison subset, and set the corresponding comparison association threshold YG respectively according to the horizontal connection situation and the vertical connection situation in the corresponding initial connection graph;

[0099] Compare and analyze the obtained comparison association data with the corresponding comparison association threshold. If the comparison association data is greater than or equal to the corresponding comparison association threshold, mark the process monitoring node corresponding to the comparison subset;

[0100] Obtain each control subset with associated markers, integrate the obtained control subsets, refine the markers in the initial connection map according to the integration result, and generate a comprehensive diagnostic knowledge link according to the refined marker result of the initial connection map. The comprehensive diagnostic knowledge link includes the association relationships existing in the impacts caused by corresponding faults of corresponding electrical automation equipment during operation on other electrical automation equipment or other production processes;

[0101] The data preprocessing unit is used to perform adaptive processing according to the equipment operation information collected based on the electrical automation equipment information and the corresponding comprehensive diagnostic knowledge link, and generate a corresponding adaptive diagnostic knowledge link according to the adaptive processing result. The specific implementation process includes:

[0102] Extract features respectively for the comprehensive diagnostic nodes corresponding to the electrical automation equipment information in the comprehensive diagnostic knowledge link according to the association marker results between the corresponding process monitoring nodes therein to obtain fault diagnosis feature data, perform sorting processing on the control association data corresponding to the association marker results between the process monitoring nodes, and generate a fault diagnosis feature data set corresponding to the corresponding fault type in the corresponding comprehensive diagnostic node;

[0103] Obtain the equipment operation information collected based on the electrical automation equipment information and the fault diagnosis feature data sets corresponding to each fault type in the corresponding comprehensive diagnostic knowledge link;

[0104] Set a fault diagnosis link, which includes the equipment operation information obtained from the corresponding electrical automation equipment information within the same time period, and the obtained equipment operation information does not distinguish between that obtained by the active acquisition unit or the passive acquisition unit;

[0105] Compare and analyze the equipment operation information corresponding in the fault diagnosis link with the comprehensive diagnostic knowledge link, extract the comprehensive diagnostic nodes where the process monitoring nodes with associated markers exist in the comprehensive diagnostic knowledge link according to the comparison and analysis result, extract according to the position information of the obtained comprehensive diagnostic nodes in the comprehensive diagnostic knowledge link, and obtain the adaptive diagnostic knowledge link corresponding to the corresponding fault diagnosis link according to the extraction result. The adaptive diagnostic knowledge link includes the adaptive fault diagnosis feature data sets corresponding to each fault type corresponding to the corresponding comprehensive diagnostic node, and the adaptive fault diagnosis feature data set includes the fault diagnosis feature data set corresponding to the equipment operation information involved in the fault diagnosis link.

[0106] The fault analysis module is used to perform fuzzy logic processing on the equipment operation information corresponding to the corresponding electrical automation equipment information according to the adaptive diagnostic knowledge link, and set an adaptive fault analysis link corresponding to the electrical automation equipment information at the current moment according to the fuzzy logic processing result;

[0107] Obtain the adaptive diagnostic knowledge link corresponding to the device operation information corresponding to the corresponding electrical automation device information, and perform fuzzy logic processing on the corresponding fault diagnostic feature data in the adaptive fault diagnostic feature data set corresponding to the obtained adaptive diagnostic knowledge link. The specific implementation process includes:

[0108] Respectively obtain the corresponding fault diagnostic feature data in the adaptive diagnostic knowledge link, and perform inductive integration according to the type of device operation information corresponding to the corresponding fault type of the corresponding electrical automation device in the corresponding comprehensive diagnostic node, and set the fault processing data set;

[0109] Obtain the number of corresponding fault diagnostic feature data in the fault processing data set and mark it as w. For the w corresponding x1, x2, …, x w ; conduct pairwise comparison and analysis to obtain the corresponding relative membership matrix gx p │x q ), and for the corresponding fault diagnostic feature data x q in the fault processing data set, obtain the membership degree data h q (x) of it relative to other objects. Among them, the membership degree data of the corresponding fault diagnostic feature data is obtained through the formula ;

[0110] Preset a fuzzy rule base, input the obtained membership degree data into the fuzzy rule base for matching analysis, and calculate the fuzzy set corresponding to the corresponding fuzzy logic operator;

[0111] Set the corresponding fuzzy processing result according to the fuzzy set corresponding to the adaptive fault diagnostic feature data corresponding to the data type of the corresponding device operation information in each comprehensive diagnostic node, and associate and store the corresponding fuzzy processing result with the corresponding adaptive diagnostic knowledge link.

[0112] The fault warning module is used to analyze and process the corresponding device operation information according to the adaptive fault analysis link obtained from the corresponding electrical automation device information. The process of generating fault detection information according to the analysis and processing results includes:

[0113] Obtain the device operation information corresponding to the corresponding electrical automation device information in the fault diagnosis link, and respectively obtain the fuzzy processing results corresponding to the fault diagnostic feature data of the process monitoring nodes associated with it in the adaptive fault analysis link according to the data type of the obtained device operation information;

[0114] Compare and analyze the obtained device operation information with the fuzzy processing results corresponding to the corresponding fault diagnostic feature data respectively to obtain a matching result. The matching result includes two types: matching and non - matching, and conduct fault diagnosis analysis according to the comparison and analysis results;

[0115] Obtain the matching tag results of the mutually associated tags within the adaptive fault analysis link, and perform data tagging on the matching tag results;

[0116] Obtain the corresponding data tagging results, and preset the corresponding fault evaluation coefficients according to the control associated data corresponding to the corresponding associated tags;

[0117] Judge whether the corresponding electrical automation equipment conforms to the corresponding fault type according to the evaluation results and the fault evaluation coefficients, traverse and analyze the fault diagnosis characteristic data corresponding to each fault type in turn, and obtain the fault type of the corresponding electrical automation equipment according to the traversal analysis results;

[0118] Output the fault detection information of the corresponding electrical automation equipment according to the obtained fault type.

[0119] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electrical automation equipment fault detection system, comprising a fault detection management center, characterized in that: The fault detection management center includes an equipment management module, a data acquisition module, a data preprocessing module, a fault analysis module and a fault warning module; The equipment management module is used to obtain the production process information corresponding to the corresponding enterprise, and obtain the corresponding electrical automation equipment information according to the production process information; The data acquisition module is used to collect equipment operation information of the electrical automation equipment information corresponding to the production process information during operation, and set the collected equipment operation information as a fault diagnosis link; The data preprocessing module is used to comprehensively process the historical equipment operation information corresponding to the corresponding electrical automation equipment information, construct a comprehensive diagnosis knowledge link of the fault type corresponding to the corresponding electrical automation equipment information, match the fault diagnosis link with the corresponding comprehensive diagnosis knowledge link, and generate an adaptive diagnosis knowledge link according to the matching result; The fault analysis module is used to perform fuzzy processing on the corresponding fault diagnosis feature data in the acquired adaptive diagnosis knowledge link, and generate an adaptive fault analysis link according to the fuzzy processing result; The fault warning module is used to compare and analyze the obtained fault diagnosis link with the corresponding adaptive fault analysis link, and generate fault detection information according to the comparison and analysis result.

2. An electrical automation equipment fault detection system according to claim 1, characterized in that: The process of obtaining electrical automation equipment information includes: The staff of the enterprise inputs the corresponding enterprise production information, which includes enterprise qualification information, enterprise registration information, production basic information and production process information, and verifies the obtained enterprise production information; Acquire the enterprise production information that has completed the verification processing, analyze and process the corresponding basic production information and production process information, and obtain the production process information corresponding to the corresponding production product information, wherein the production process information includes the production process information corresponding to the corresponding product, the production equipment information corresponding to the production process information, and the production equipment utilization information corresponding to the production process information. According to the production process information, obtain the electrical automation equipment information corresponding to the corresponding production process information, and generate the corresponding process monitoring node.

3. An electrical automation equipment fault detection system according to claim 2, characterized in that: The process of obtaining the equipment operation information of the corresponding electrical automation equipment during operation includes: An operation monitoring terminal is provided in each electrical automation device, and the operation monitoring terminal is used to collect equipment operation information of the electrical automation device and is interconnected with the corresponding process monitoring node; Setting up a passive collection unit and an active collection unit; The passive collection unit is used for the staff to input the collection demand information and obtain the equipment operation information corresponding to the corresponding electrical automation equipment according to the collection demand information; The active collection unit is used to collect the equipment operation information of the corresponding electrical automation equipment in the production process information in real time; The equipment operation information includes electrical parameter data, equipment status data and historical fault data, and the obtained equipment operation information is marked according to the corresponding collection time and electrical automation equipment information.

4. An electrical automation equipment fault detection system according to claim 3, characterized in that: The process of setting up a fault diagnosis link based on device operation information includes: Set the fault diagnosis cycle, obtain the marking results of the equipment operation information, obtain the equipment operation information whose corresponding collection time belongs to the fault diagnosis cycle, sort and process it in sequence according to the process monitoring nodes to which the equipment operation information belongs, integrate the obtained equipment operation information according to the sorting results, generate the corresponding fault diagnosis link, and temporarily store the obtained equipment operation information through the fault diagnosis link.

5. The electrical automation equipment fault detection system according to claim 4, characterized in that: The process of building a comprehensive diagnostic knowledge chain of fault types corresponding to the corresponding electrical automation equipment information includes: The obtained production process information is used to set production process links according to the corresponding process monitoring nodes, the overlapping of electrical automation equipment belonging to the corresponding process monitoring nodes in each production process link is marked, and comprehensive diagnosis nodes are set according to the corresponding electrical automation equipment; According to the comprehensive diagnosis nodes corresponding to the corresponding production process links, the corresponding process monitoring nodes in the corresponding comprehensive diagnosis nodes are connected horizontally, and the initial connection map is generated; Perform association processing according to the connection status of the corresponding process monitoring node in the initial connection map, and obtain the historical status data set corresponding to the corresponding fault type of the corresponding process monitoring node, wherein the historical status data set includes historical status data and corresponding analysis results; According to the horizontal connection status and vertical connection status of the corresponding process monitoring nodes, corresponding horizontal control subsets and vertical control subsets are set respectively; Obtaining control association data corresponding to the corresponding horizontal control subset and the vertical control subset, and setting corresponding control association thresholds according to the horizontal connection situation and the vertical connection situation in the corresponding initial connection map; Compare and analyze the obtained control association data with the corresponding control association threshold, and associate and mark the corresponding process monitoring nodes in the corresponding control subset according to the comparison analysis results; Marking is performed in the initial connection map according to the associated labeling results to generate a comprehensive diagnostic knowledge link.

6. An electrical automation equipment fault detection system according to claim 5, characterized in that: The process of generating adaptive diagnostic knowledge links includes: Obtain the fault diagnosis link, match and analyze the equipment operation information stored in the fault diagnosis link with the comprehensive diagnosis knowledge link, and extract the process monitoring nodes with associated marks on the corresponding comprehensive diagnosis nodes in the comprehensive diagnosis knowledge link according to the matching analysis results; The connection relationship and position information within the comprehensive diagnosis knowledge link to which the obtained process monitoring node belongs are extracted, and the corresponding adaptive diagnosis knowledge link is obtained according to the extraction result.

7. An electrical automation equipment fault detection system according to claim 6, characterized in that: The process of generating an adaptive fault analysis link includes: Acquire an adaptive diagnosis knowledge link corresponding to a corresponding fault diagnosis link, wherein the adaptive diagnosis knowledge link includes fault diagnosis feature data corresponding to a corresponding fault type of the corresponding electrical automation equipment; According to the connection status of the process monitoring nodes corresponding to the fault types in the adaptive diagnosis knowledge link, the corresponding fault diagnosis feature data are summarized and integrated to set the fault processing data set; Performing pairwise comparative analysis on the corresponding fault diagnosis feature data in the fault processing data set according to the corresponding data types, obtaining the corresponding relative membership matrix, and obtaining the membership data corresponding to the corresponding data type in the corresponding fault processing data set according to the obtained relative membership matrix; A fuzzy rule base is preset, and the obtained membership data is input into the fuzzy rule base for matching analysis, and the fuzzy sets corresponding to the corresponding fuzzy logic operators are calculated; The fuzzy processing results of each fault diagnosis feature data in the corresponding adaptive diagnosis knowledge link are set according to the corresponding fuzzy set, and the corresponding fuzzy processing results are associated with the corresponding adaptive diagnosis knowledge link and stored to generate an adaptive fault analysis link.

8. An electrical automation equipment fault detection system according to claim 7, characterized in that: The process of generating fault detection information includes: Obtain the equipment operation information corresponding to the corresponding electrical automation equipment information in the fault diagnosis link, compare and analyze the obtained equipment operation information with the fuzzy processing results of the corresponding fault diagnosis feature data in the adaptive diagnosis knowledge link, obtain the matching results of the corresponding fault types, and perform data marking on the matching marking results; Obtain the data labeling results corresponding to the corresponding fault types in the adaptive fault analysis link, and preset the corresponding fault assessment coefficients according to the comparison correlation data corresponding to the correlation labels between the corresponding process monitoring nodes in the adaptive fault analysis link; Determine whether the corresponding electrical automation equipment meets the corresponding fault type based on the evaluation results and fault evaluation coefficients of the corresponding process monitoring nodes; Perform traversal analysis on the fault diagnosis feature data corresponding to each fault type in turn, and obtain the fault type of the corresponding electrical automation equipment according to the traversal analysis results; Output the fault detection information of the corresponding electrical automation equipment according to the obtained fault type.

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